Overview: GMP manufacturing and quality operations generate complex webs of data spanning materials, samples, batches, equipment, processes, people, and quality events. Traditional relational systems can manage this data effectively, but as organizations scale, connecting information across these domains can make provenance, investigations, change management, and data reuse increasingly difficult. Through practical GMP use cases, we’ll show how graph architecture can provide complete provenance for chain-of-custody and chain-of-identity, accelerate investigations and CAPA, and simplify regulatory audits without reconstructing context across disconnected records.
We’ll also explore how configuration versioning and change histories can support Computer Software Assurance (CSA) and more focused revalidation when systems change. We’ll then examine how graph architecture provides a complementary foundation for LIMS in GMP manufacturing by representing data and the relationships between it as first-class elements.
Attendees will learn:
- Key graph concepts and terminology, and how graph-based architecture compares with traditional relational approaches
- How the underlying data model supports broader data strategies
- How well-structured, graph-ready data provides a foundation for FAIR data and future applications of LLMs, AI, and advanced analytics
- How to assess graph readiness, what good data looks like, and where organizations commonly encounter challenges
